1,720,983 research outputs found
Reproducibility of predictive networks for mouse visual cortex
No full textDeep predictive models of neuronal activity have recently enabled several new
discoveries about the selectivity and invariance of neurons in the visual
cortex. These models learn a shared set of nonlinear basis functions, which are
linearly combined via a learned weight vector to represent a neuron's function.
Such weight vectors, which can be thought as embeddings of neuronal function,
have been proposed to define functional cell types via unsupervised clustering.
However, as deep models are usually highly overparameterized, the learning
problem is unlikely to have a unique solution, which raises the question if
such embeddings can be used in a meaningful way for downstream analysis. In
this paper, we investigate how stable neuronal embeddings are with respect to
changes in model architecture and initialization. We find that
regularization to be an important ingredient for structured embeddings and
develop an adaptive regularization that adjusts the strength of regularization
per neuron. This regularization improves both predictive performance and how
consistently neuronal embeddings cluster across model fits compared to uniform
regularization. To overcome overparametrization, we propose an iterative
feature pruning strategy which reduces the dimensionality of
performance-optimized models by half without loss of performance and improves
the consistency of neuronal embeddings with respect to clustering neurons. This
result suggests that to achieve an objective taxonomy of cell types or a
compact representation of the functional landscape, we need novel architectures
or learning techniques that improve identifiability. We will make our code
available at publication time
Leading by example: Guiding knowledge transfer with adversarial data augmentation
No full textKnowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, it has recently been shown that this method is unable to transfer simple inductive biases like shift equivariance. To extend existing functional transfer methods like KD, we propose a general data augmentation framework that generates synthetic
data points where the teacher and the student disagree. We generate new input data through a learned distribution of spatial transformations of the original images. Through these synthetic inputs, our augmentation framework solves the problem of transferring simple equivariances with KD, leading to better generalization. Additionally, we generate new data points with a fine-tuned Very Deep Variational Autoencoder model allowing for more abstract augmentations. Our learned augmentations significantly improve KD performance, even when compared to classical data augmentations. In addition, the augmented inputs are interpretable and offer a unique insight into the properties that are transferred to the student
Leading by example: Guiding knowledge transfer with adversarial data augmentation
No full textKnowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, it has recently been shown that this method is unable to transfer simple inductive biases like shift equivariance. To extend existing functional transfer methods like KD, we propose a general data augmentation framework that generates synthetic
data points where the teacher and the student disagree. We generate new input data through a learned distribution of spatial transformations of the original images. Through these synthetic inputs, our augmentation framework solves the problem of transferring simple equivariances with KD, leading to better generalization. Additionally, we generate new data points with a fine-tuned Very Deep Variational Autoencoder model allowing for more abstract augmentations. Our learned augmentations significantly improve KD performance, even when compared to classical data augmentations. In addition, the augmented inputs are interpretable and offer a unique insight into the properties that are transferred to the student
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Convolutional neural network models of the primate retina reveal adaptation to natural stimulus statistics
No full textAppropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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